40 Questions DevOps Engineers Should Ask Before Trusting AI Infrastructure Outputs
When Datadog AI suggests new alert thresholds or GitHub Copilot generates a terraform module, your split-second approval shapes what runs in production. Asking the right questions before accepting AI outputs protects your infrastructure from becoming a black box that only machines understand.
These are suggestions. Use the ones that fit your situation.
1When GitHub Copilot suggests a security group rule or IAM policy, can I explain to another engineer why each permission is necessary for this specific workload?
2Does the terraform or CloudFormation generated by AWS CodeWhisperer match the actual blast radius if this resource fails, or have I just accepted defaults?
3If ChatGPT generates a Kubernetes manifest with resource requests and limits, do those numbers reflect my actual traffic patterns or are they generic placeholders?
4When Copilot auto-completes a Docker build step or installation command, do I know what version it pulled and whether that version has known vulnerabilities?
5Does the networking configuration AI suggested for my microservices actually match how my services communicate, or does it assume a different deployment topology?
6If AI generated a load balancer configuration, have I verified that the health check endpoints and timeouts work for my actual application startup time?
7When accepting AI-suggested storage configurations (S3 bucket policies, EBS volume types, RDS parameter groups), do I understand the availability and durability trade-offs?
8Does the logging and tracing configuration generated by AI actually send data to the observability tools my team actually uses?
9If AI suggested a backup strategy, do I know how long recovery would actually take and whether that meets my RTO requirements?
10When CodeWhisperer suggests a CI/CD pipeline step, does it assume secrets management practices that match my organisation's actual policies?
Incident Response and Runbook Automation
11When PagerDuty AI suggests an automated remediation action, what happens if the root cause is something the automation was not designed for?
12Does the incident runbook generated by ChatGPT include the specific command syntax for my version of the tools I actually run, or generic examples?
13If AI auto-generates steps to restart a service or clear a queue, have I tested those steps in staging to confirm they do not leave the system in a broken state?
14When an AI suggests rolling back a deployment, does it account for data migrations or state changes that happened during the incident?
15Does the incident response runbook AI suggested include the decision points where a human needs to assess whether to continue or stop?
16If Datadog AI recommends scaling up infrastructure during an incident, do I understand the cost implications and whether that is the correct response?
17When AI suggests a database query to diagnose a problem, do I know whether that query will lock tables or impact production performance?
18Does the escalation path in an AI-generated runbook match the on-call rotations and expertise levels my team actually has?
19If AI suggests disabling alerts as a temporary measure, do I know exactly which alerts, and do I have a reminder set to re-enable them?
20When an incident runbook tells me to check a specific log file or metric, have I verified that log file exists and is populated in my actual environment?
Monitoring, Alerting, and Observability
21When Datadog AI suggests new alert thresholds, does it account for the seasonal or time-based traffic patterns specific to my application?
22If AI recommends adding a metric or changing how you collect data, does that change break any dashboards or reports other teams depend on?
23When AWS CloudWatch or Datadog AI suggests anomaly detection, do I understand what constitutes an anomaly in my system, or am I just trusting a black box?
24Does the alert fatigue reduction AI promised actually result in my on-call engineers responding to fewer false positives, or just different noise?
25If AI suggests a new SLI or SLO based on my data, does that metric actually capture what matters to my users or internal stakeholders?
26When Copilot suggests monitoring code snippets, do those snippets report to the right observability backend and in the right format?
27Does the log aggregation configuration AI suggested actually capture errors from all the services I care about, or just the obvious ones?
28If Datadog AI recommends correlating two metrics to predict failures, do I understand the causation or am I just acting on correlation?
29When AI suggests a new alerting rule, have I confirmed that it will not trigger during maintenance windows or expected operational events?
30Does the observability setup AI recommended scale to my actual volume of logs, metrics, and traces, or will costs spiral if traffic grows?
System Understanding and Operational Continuity
31After accepting AI-generated infrastructure changes, can I draw a diagram of how data flows through the system without consulting the AI again?
32If the engineer who wrote the original system is no longer on the team, does accepting AI-generated modifications mean no one fully understands it now?
33When GitHub Copilot suggests a pattern or approach, have I considered whether it matches the patterns already established in my codebase?
34If AI generates configuration for a critical path service, can a junior engineer on my team understand it well enough to troubleshoot at 3am?
35Does the infrastructure AI suggested have failure modes that are invisible until something goes wrong in production?
36When accepting AI-optimised configurations, am I losing the design constraints and trade-off decisions that the original architect made?
37If I stop using AI tools tomorrow, would my team still be able to modify and debug the infrastructure AI helped build?
38Does the AI-suggested approach introduce dependencies on cloud provider features that might not be portable to other platforms?
39When AI generates incident response procedures, does it document the reasoning and assumptions, or just the steps?
40Have I created a process where new team members learn system reliability from understanding real decisions, or from reading AI-generated explanations?
How to use these questions
Before accepting any AI-generated infrastructure code, ask yourself: could I defend this configuration in a security review or post-incident analysis? If the answer is no, you do not fully understand it yet.
Use AI as a starting point, not an endpoint. Treat Copilot suggestions and ChatGPT outputs like drafts that need your signature, not finished products.
Keep a log of which AI-generated configurations you modified and why. This builds a record of your actual system design decisions instead of hiding them inside AI black boxes.
Test AI-generated runbooks in staging exactly as written before an incident forces you to run them. If they fail in a safe environment, they will fail when you need them most.
Set a rule: if two team members cannot explain why a piece of infrastructure exists, it was probably accepted from AI without sufficient judgement.